A recent advancement in the world of heterogeneous computing, the NVLink interconnect enables high-speed communication between GPUS and CPUs and among GPUS. In this paper we show how NVLink changes the role GPUS can play in graph, and more in general, data analytics. With the technology preceding NVLink, the processing efficiency of GPUS is limited to data sets that fit into their local memory. The increased bandwidth provided by NVLink imposes a reassessment of many algorithms - including those used in data analytics - that in the past could not efficiently exploit GPUS because of their limited bandwidth towards host memory. Our contributions consist in the introduction of the basic properties of one of the first systems using NVLink, and the description of how one of the most pervasive data analytics kernels, SpMV, can be tailored to the system in question. We evaluate the resulting SpMV implementation on a variety of data sets, and compare favorably to the best results available in the literature.